A manifesto for model merging

  • Authors:

  • Greg Brunet;Marsha Chechik;Steve Easterbrook;Shiva Nejati;Nan Niu;Mehrdad Sabetzadeh

  • Affiliations:

  • University of Toronto, Toronto, Ontario, Canada;University of Toronto, Toronto, Ontario, Canada;University of Toronto, Toronto, Ontario, Canada;University of Toronto, Toronto, Ontario, Canada;University of Toronto, Toronto, Ontario, Canada;University of Toronto, Toronto, Ontario, Canada

  • Venue:
  • Proceedings of the 2006 international workshop on Global integrated model management
  • Year:
  • 2006

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Abstract

If a modeling task is distributed, it will frequently be necessary to merge models developed by different team members. Existing approaches to model merging make assumptions about the types of model to be merged, and the nature of the relationship between them. This makes it hard to compare approaches. In this paper, we present a manifesto for research on model merging. We propose a framework for comparing different approaches to merging, by treating merge as an algebraic operator over models and model relationships. We specify the algebraic properties of an idealized merge operator, as well as related operators such as match, diff, split, and slice. We then show how our framework can be used to compare existing approaches by applying it to two of our own research projects on model merging. We show how this analysis permits a detailed comparison of approaches, reveals the key features of each, and identifies weaknesses that require further research. Most importantly, the framework emphasizes the need to make explicit all assumptions about the relationships between models, and indeed to treat model relationships as first class objects.